Special Issue "Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting"
A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 October 2016)
Prof. Dr. Wei-Chiang Hong
1. Department of Information Management, Oriental Institute of Technology, No. 58, Sec. 2, Sichuan Rd., Panchiao, Taipei, 220, Taiwan
2. Distinguished Professor at School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou, China
Website | E-Mail
Interests: computational intelligence (neural networks; evolutionary computation); application of forecasting technology (ARIMA; support vector regression; chaos theory)
The development of kernel methods and hybrid evolutionary algorithms (HEA) to support experts in business forecasting is of great importance to improve the accuracy of the actions derived from an energy decision maker, and that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required while decisions are made in a competitive environment. Therefore, this is of special relevance in the big data era; these forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking of ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfied parameters in forecasting models. Another issue to be addressed is that of seasonality or cyclicity of energy data, and the dynamic nonlinearity of the data in demanding process itself.
This Special Issue aims to attract researchers with an interest in the research areas described above. Specifically, we are interested in contributions towards the development of HEAs with kernel methods or with other novel methods (chaos theory, fuzzy theory, cloud theory, quantum behavior, and so on), which, with superior capabilities over the traditional optimization approaches, aims to overcome some endogenous drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy. As an example, genetic algorithms with simulated annealing algorithms (GA-SA), by applying the superior capability of SA algorithm to reach more ideal solutions, and by employing the mutation process of GA to enhance the searching process. The new hybrid evolutionary algorithms require more detailed research and empirical studies. On the other hand, some other new trials, namely combined approaches, such as seasonal mechanism or multiple seasonal mechanism that are combined with forecasting models, are also welcome.
All submissions should be based on the rigorous motivation of the mentioned approaches, and all the developed models should also have a corresponding theoretical sound framework, lacking such a scientific approach is discouraged. Validation support of existing/presented approaches is encouraged to be done using real practical applications.
Prof. Dr. Wei-Chiang Hong
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access monthly journal published by MDPI.
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Support vector regression